Inter-strain diversity among coastal picocyanobacteria across salinity gradients

Maximilian Berthold; Naaman M. Omar; Alyson MacCormack; Mireille Savoie; Martin Hagemann; Douglas A. Campbell

doi:10.1371/journal.pone.0335835

Abstract

Strains of the picocyanobacteria genera Synechococcus and Cyanobium occur in cold-temperate zones, from coastal waters to the open ocean, spanning a range of salinities and water depths. To thrive across different salinities, picocyanobacteria strains acclimate internal turgor by expending metabolic energy to accumulate compatible solutes and extrude inorganic ions. We grew five picocyanobacterial strains, derived from a range of source habitat salinities, under a matrix of light and salinity levels. Although chlorophyll is a general proxy for phytoplankton biomass, Chl/cell is itself a key component of acclimation. Indeed, for all strains, except the brackish water CZS48M, the conditions supporting the highest Biovolume-specific growth rates shifted upwards towards higher salinities and PAR, compared to conditions for maximum Chl-specific growth. Therefore, the apparent optimal niche for a strain varies depending upon the metric used to track growth. Strains CZS48M and CZS25K derived from brackish lagoons of the Baltic Sea may be true ‘brackobionts,’ with maximal growth at brackish salinities and high PAR, coinciding with conditions for their maximal metabolic capacities. CZS48M and CZS25K both also show high metabolism under higher salinity and low PAR, suggesting a situation of rapid metabolism, but lower achieved growth, under stress at the edges of their environmental tolerance ranges. Conversely, NIES981 (full marine, East China Sea) shows rapid metabolism under high, but also under low, salinities. The preferred salinity of each strain is consistent with its genome-encoded capabilities to synthesize different compatible solutes. In general, across the strains, maximal metabolic rate is often offset from growth rate optima, showing that conversion of metabolic electrons to biomass varies widely depending upon strain and condition, likely partly reflecting the costs of turgor regulation at the limits of salinity tolerance.

Citation: Berthold M, Omar NM, MacCormack A, Savoie M, Hagemann M, Campbell DA (2026) Inter-strain diversity among coastal picocyanobacteria across salinity gradients. PLoS One 21(10): e0335835. https://doi.org/10.1371/journal.pone.0335835

Editor: Balaji Prasath Barathan, Gujarat Institute of Desert Ecology, INDIA

Received: October 16, 2025; Accepted: September 15, 2026; Published: October 7, 2026

Copyright: © 2026 Berthold et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The minimal data set is available at figshare via DOI: 10.6084/m9.figshare.32536950.

Funding: Natural Sciences and Engineering Research Council of Canada, Dr. Douglas A. Campbell Canada Foundation for Innovation, Dr. Douglas A. Campbell New Brunswick Foundation for Innovation, Dr. Douglas A. Campbell Deutsche Forschungsgemeinschaft, Dr. Maximilian Berthold.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Coastal waters worldwide contain a global brackish microbiome [1], which is in part dominated by cyanobacteria, the only prokaryotic phylum capable of performing oxygenic photosynthesis. According to their form of RubisCO and carboxysome structure cyanobacteria are divided into alpha- and beta-cyanobacteria [2]. The alpha-cyanobacteria form an evolutionary clade, described as picocyanobacteria because of generally smaller cell sizes compared to beta-cyanobacteria. Picocyanobacteria are ubiquitous primary producers, with diverse strains exploiting different ecological niches, realized with comparatively small genome sizes for individual strains. Strains of the picocyanobacteria genera Synechococcus and Cyanobium frequently occur in cold-temperate systems, from coastal waters to the open ocean, where they contribute substantially to annual global CO₂ fixation [3]. Synechococcus and Cyanobium are in turn divided into several clades based upon cell diameter, DNA base composition, pigment composition, and ecophysiological traits, with different clades occurring across a range of salinities and water depths [4,5]. Awareness of the wide distribution of these picocyanobacteria is increasing with metagenomic approaches [6], which notably show a changing picocyanobacterial genome repertoire in the saline gradient of the brackish Baltic Sea [7].

To thrive in ecosystems of different salinities, spanning hypersaline, marine, brackish or freshwater habitats, cyanobacteria need to adapt or acclimate internal turgor toward external salinity. Salt acclimation has been analyzed mainly among the beta-cyanobacteria, which accumulate specific compatible solutes and actively extrude inorganic ions upon salt stress [8]. Specific compatible solutes determine maximum salt tolerance; for example, freshwater and terrestrial strains typically accumulate sucrose and/or trehalose, while marine strains accumulate glucosylglycerol and glucosylglycerate; whereas strains from hypersaline environments accumulate glycine betaine or homoserine betaine [9–11]. Genes for glycosyl glycerol synthesis are frequently found in marine picocyanobacteria of the genus Synechococcus [12], whereas only one Prochlorococcus strain, from the Red Sea, has been detected with expressed genes for glycosyl glycerol synthesis [13]. Laboratory analyses of a few alpha picocyanobacteria revealed that glycosyl glycerol, glycosyl glycerate, but also glycine betaine, are synthesized upon salt stress in Synechococcus spp., while Prochlorococcus spp. responded to different salinities by accumulating sucrose, glycosyl glycerate and/or glycine betaine [14].

It is likely that several picocyanobacteria from one clade can form meta-populations of genetically slightly distinct strains which, through their inter-strain diversity, are able to exploit a wider range of environmental conditions, including salinities, temperature, or light [15]. Such strain diversity of picocyanobacteria is ecologically relevant, as shifts in the composition of communities may represent sentinels of a warming future [16–18], which will likely benefit smaller phytoplankton cells. Community shifts towards smaller cells have several implications for the functioning of ecosystems, including grazing evasion [19], altered sinking [20], and increased optical attenuation per unit of phytoplankton biomass [21,22]. These characteristics of small picocyanobacteria impact biogeochemical cycles and nutrient sequestration rates, other phytoplankton taxa, and ultimately the transfer of carbon through the food web. Indeed, recent studies suggest that a community shift towards dominance by small phytoplankton caused a decline in mussel size in the Baltic Sea, likely as a response to lowered energy transfer from small phytoplankton [23]. Ecophysiological characterization of different strains, coupled with analyses of their underlying genomic capacities, can thus contribute to improved predictive models of when and where such strains can be expected to rise to dominance. Efficient approaches are thus necessary to test and functionally characterize the rapidly accumulating numbers of available, identified, and even sequenced strains.

Picocyanobacterial strains have been clustered into genomic clades, primarily by using 16s rDNA [24,25], or grouped by physiological characters, such as pigment composition [4,26], although both approaches may fail to capture the ecological niches of strains. We herein implement a high-throughput approach to rapidly describe the growth optima and photophysiologies of picocyanobacteria, starting with a case study of five Cyanobium or Synechococcus strains, which are closely related based on conserved sequences including 16s rDNA, but which originate from geographically and ecologically diverse regions of the Gulf of Mexico, Baltic Sea, Chesapeake Bay, and the coastal Pacific. We chose these particular strains as they were either isolated from similar ecosystems, or have been included in physiological or genomic studies elsewhere, allowing us to validate our findings. Our efforts are motivated by the rising described omnipresence of picocyanobacteria within the brackish microbiome [27]. We hypothesize that phenotypically similar, but genotypically and physiologically distinct strains, show distinct growth optima across salinity gradients. We show that strain-specific salinity optima can be differentially expressed in terms of fastest growth rates, highest quantum yield, or metabolic rate.

Materials and methods

Strains, culturing, & media preparation

We chose five picocyanobacterial strains (see Table 1 for more information) to test differential responses to a matrix of salinities (parts per thousand, ppt) and Photosynthetically Active Radiation (PAR, μmol photons m^-2 s^-1). Two strains (CZS25K & CZS48M) were isolated from the Baltic Sea at a brackish lagoon in Germany (Darss-Zingst Lagoon System), but were previously maintained in freshwater BG11 media. One strain CCMP1333 (WH5701) was isolated from the Long Island Sound, USA, and selected because of close 16s rDNA homology with the two Baltic lagoon strains. CCMP1333 is also one of the earlier completely sequenced cyanobacterial genomes. CCMP836 was isolated from the Gulf of Mexico, grows in full marine media, but shows pigment characteristics and 16S rDNA sequences close to the Baltic strains [4,5]. CCMP836 and CCMP1333 are both part of subcluster 5.2 [28]. NIES-981 was isolated close to Hokkaido island, Japan, grows in full marine media, but again shows a 16s rDNA sequence similar to the Baltic strains (assigned clade group K [29],). Strains were received in fully enriched BG11 media [30,31] made with either freshwater (CZS25K and CZS48M) or full saline artificial seawater (CCMP1333, CCMP836, NIES981). Subsequently, CZS25K and CZS48M were shifted to BG11 at a brackish salinity of 4 ppt to increase the growth rates of mother inoculant cultures. All strains were pre-cultured on a shaker, at 22 ℃, with 12:12 light:dark at 60–80 µmol photons m^-2 s^-1 from white fluorescent bulbs. Pre-cultures were diluted 1–4 in fresh media every seven days to keep them actively growing. We chose BG11 media as it contains sufficient Nitrogen (N) in the form of nitrate to keep batch cultures nutrient replete, although using nitrate instead of ammonium as a primary N source impacts the growth and physiology of the cells [32]. BG11 media with different salinities were created using artificial sea water mixed with deionized water (Milli-Q) to salinity levels of 1, 4, 11, 18, 25 and 32 ppt. The media tends to flocculate with increasing salinities due to increasing pH. Thus, we titrated each media back to the recommended pH of 7.5 using 1 M hydrochloric acid, upon which flocculation and precipitates re-dissolved. We did not tightly control the media dissolved inorganic carbon (DIC), but the BG11 was prepared with an initial concentration of 0.189 mM NaHCO₃. The atmospheric CO₂ in the culture chambers was approximately 750 ppm, leading to an estimated initial equilibrium [DIC] at pH 7.5 of ~1356 mM [33].

Download:

Table 1. General information on strains used in this study.

https://doi.org/10.1371/journal.pone.0335835.t001

Experimental design; absorbance and fluorescence measurement regimes and data handling

We used 24-well plates (Falcon, 2.5 ml volume per well, No. 351147) to culture each strain at six different salinity levels (six columns) with four replicates (four rows) at each of three light levels. Each plate run was then replicated three times within 8 weeks, making a total of three separate plate replicates with four technical well replicates per each combination of strain, light level, and salinity level. Three plates for one strain were simultaneously cultured at 30, 100, and 300 µmol photons m^-2 s^-1 in a common growth chamber for consistency across different growth PAR. Wells each held one magnetizable micro-stir bar (V&P Scientific Inc.). Each day cells in well plates were resuspended using an external magnet (V&P Scientific Inc.) and then immediately measured in a CLARIOstar (BMG LABTECH) in top read mode, with an excitation at 440 nm for a fluorescence emission spectral scan from 500 to 750 nm, and an absorbance spectral scan, 400–750 nm. BG11 media with different salinities had different inherent OD values. We read plates with media alone before every inoculation to correct for well and media-specific absorption and thus blank-correct each culture across the spectral scans. Furthermore, batches of BG11 media had slightly different salinities, so each batch was assigned a unique ID to track final achieved salinity in each culture well.

For import, tidying, analyses and presentation of data we used R [36] running under RStudio [37] and packages [38–45].

Cell count & chlorophyll calibrations

From each plate, after each doubling of Chlorophyll a (Chl) content, tracked as OD_680nm – OD_750nm, plates were opened in a culture hood and a culture from each replicate row of wells, representing all six salinity levels, were harvested for further analyses of Chl a, cell number, and photophysiological characteristics. For Chl a, 20 µl of culture were mixed with 2 ml of 90% acetone:DMSO (3:2 ratio), incubated for 30 min in the dark and analyzed with a Turner Trilogy (Turner Designs, San Jose, CA, USA) fluorometer, calibrated using Chl a standards, with relative fluorescence units converted to Chl a in µg mL^-1. These Chl a concentrations were compared to respective (OD_680nm – OD_750nm) values from the same sample and day to generate strain- and growth light-specific regression models for converting daily (OD_680nm – OD_750nm) to absolute [Chl a] (S1 Table). Chl content is a component of photoacclimation, so in parallel we counted cells from 10 µl samples of culture using a hemocytometer and a Leica microscope at 400x magnification. Those cell counts were compared to the respective OD_750nm values from the same sample and day to generate strain- and growth light-specific regression models for converting daily OD_750nm to cells mL^-1 (S2 Table). Cell biovolumes varied across the strains and growth conditions, so we estimated total biovolume trajectories by multiplying cell counts by strain- and condition-specific biovolumes. We were thus able to calculate Chl- and biovolume-specific growth rates for each strain.

Growth analyses

The daily well specific [46] measures of [Chl a], derived from strain- and light-specific regressions of (OD_680nm – OD_750nm), and Cell Counts, from regressions of OD_750nm, were fit using a modified Gompertz model, chosen to account for any lag-phase. The modified Gompertz model as described by Zwietering et al. [47]is given as:

(1)

where A is the maximum population size [], λ is the lag time, and 𝜇_m is the maximum specific growth rate.

Modified Gompertz growth curves were fit using non-linear model fitting functions [39]. Growth models were fit separately for each strain, salinity and plate replicate (n = 3) based on the pooled four (start of experiment) to two (end of experiment) technical replicate wells per plate for each salinity (S3 Table).

Photophysiology

The remaining 2 mL per well harvest was used for photophysiological characterizations of each culture, at every salinity and light level, repeatedly across the growth trajectories. We used Fast Repetition Rate fluorometry [48] (FRRf, Solisense, USA) to apply series of flashlets to drive induction/relaxation trajectories. We used a double tap protocol [49], where an FRRf induction/relaxation trajectory is applied on top of an actinic light level, immediately followed by another induction/relaxation after 1 s in the dark, to allow re-opening of PSII. Actinic light steps of 10 s were applied at 0, 20, 40, 80, 160, and 320 µmol photons m^-2 s^-1, at 445 or 590 nm. FRRf excitation flashlets were applied at the same waveband, 445 or 590 nm, as the actinic light steps.

We used the onboard Solisense LIFT software to fit an induction/relaxation model [48] for each FRRf induction/relaxation trajectory. From the model fits of the inductions, we took the maximum quantum yield of Photosystem II (PSII) (F_V/F_M) as the photochemical capacity of the cultures. The relaxations were fit with a three-component exponential decay model, generating lifetimes τ₁, τ₂, and a slow τ₃ of small amplitude. We calculated an amplitude weighted average of τ₁ and τ₂ for the re-opening of PSII by downstream electron transport as a proxy for downstream metabolic capacity to use electrons.

Genomic analyses

Predicted protein sequences derived from the genomes of Cyanobium spp. (strains CZS25K, CZS48M, and NIES981) and Synechococcus spp. (strains CCMP836 and WH5701) were obtained from NCBI [34,35]. We used gene sequence probes (S4 Table) to search the genomes for the presence of known genes encoding proteins involved in osmolyte production and ion transport, using BLAST+ v2.13.0 [50] with an e-value of 1*10⁻¹⁰ as a cutoff for genes homologous to the probe sequences. Counts of detected hits were pooled based on gene name and used in subsequent analyses.

Generalized additive modelling

Estimated exponential growth rates specific for [Chl a] and for biovolume were used to generate generalized additive models [51,52] for growth of each strain, with salinity and light level as explanatory variables (S5 and S6 Tables). Predicted growth rate values from the GAM models were filtered out if the standard error on the estimate was greater than 15% of the fitted value. We were thus able to generate modelled response surfaces of growth rates for each strain across the matrix of tested salinity and light growth conditions. We present strain-specific GAM estimates for growth optima by plotting the top 10% fastest predicted growth rates for strain vs. salinity and light. In parallel, we plotted the strain which had the highest GAM estimated growth rate under a given combination of salinity and light, for a ‘winner takes it all’ approach to define which strain would potentially dominate under given conditions.

In parallel, we fit GAM of F_V/F_M (S7 Table) as a metric of photochemical potential across the matrix of conditions. For metabolic capacity across conditions, we took the weighted-average 𝜏_PSII from the amplitudes and duration of the 𝜏₁ and 𝜏₂ extracted from the fits of the re-opening of PSII after closure, taken after 1 s of darkness, immediately after actinic light steps closest to the respective growth light, for each salinity condition and strain. For better conceptual comparability with growth rates, for fitting GAM (S8 Table) we used the reciprocal 1/𝜏_PSII which is the rate constant (s^-1) for electrons transported away from PSII. Again, for presentation of the GAM model for optimal conditions for each strain, we chose the upper 10% of F_V/F_M or 1/𝜏_PSII across growth conditions and strain. Biovolume-specific growth rates were compared to F_V/F_M and 1/𝜏_PSII, to uncover patterns in the conversion of photochemistry and metabolic electrons to biomass across salinity and PAR (S9 Table).

Results

Salinity-light niches for chlorophyll- and biovolume-specific growth rates across strains

We summarized growth rates for the strains across the matrix of PAR and salinity conditions using GAM (Fig 1 and Fig 2). To identify the optimal growth conditions for each strain, we plot the regions where each given strain achieved the top 10% of their Chl-specific, or Biovolume -specific, growth rates (Fig 1). Individual GAMs for each strain can be found in the Supplement (S11–S15 Figs).

Download:

Fig 1. Comparison of Conditions supporting Maximum Biovolume-Specific or Maximum Chlorophyll-Specific growth rates (d^-1) of picocyanobacterial strains, growing across a matrix of salinities (ppt) and light levels (PAR, µmol photons m^-2 s^-1, µE).

Each panel shows growth results from a different strain, ordered top to bottom, from low to high salinity tolerance. Red regions show the conditions where a strain achieved the top 10% of its Biovolume-specific growth rates, while green regions show the conditions where a strain achieved the top 10% of its Chl-specific growth rates. The growth rates used for Generalized Additive Modelling were taken from Gompertz fits of changes in total biovolume (µm³ L^-1) or total Chl (µg L^-1) over time (d^-1) under the respective PAR and salinities for each strain.

https://doi.org/10.1371/journal.pone.0335835.g001

Download:

Fig 2. Comparisons of Chlorophyll- or Biovolume-specific growth rates (d^-1) of picocyanobacterial strains growing across a matrix of salinities (ppt) and light levels (PAR, µmol photons m^-2 s^-1).

For each combination of light level (PAR, µmol photons m^-2 s^-1) and salinity (ppt), the strain (colour), cultured separately, achieving the highest Chl-specific growth rate (d^-1) or biovolume-specific growth rate, is indicated by shading, in a ‘winner takes it all’ comparison of potential competitive advantage among strains across conditions. The growth rates used for Generalized Additive Modelling were taken from Gompertz fits of changes in total biovolume (µm³ L^-1) or total Chl (µg L^-1) over time (d^-1) under the respective PAR and salinities for each strain.

https://doi.org/10.1371/journal.pone.0335835.g002

Chl-specific growth rates of CZS48M (brackish, Baltic Sea) were highest at salinities of 0–15 ppt, and at high light of 220−300 µmol photons m^-2 s^-1. Similarly, the Biovolume-specific growth rates of CZS48M were highest around salinities of 0–15 ppt and again at higher PAR of 175–300 µmol photons m^-2 s^-1, in contrast to all other strains that showed offsets in the light levels for maximal Chl a vs. biovolume growth rates (Fig 1).

Chl-specific growth rates of CZS25K (brackish, Baltic Sea) were highest around salinities of 5–10 ppt, extending across the tested light gradient of 30–300 µmol photons m^-2 s^-1. Interestingly, the highest Biovolume-specific growth rates of CZS25K were shifted upwards to salinities of 20–25 ppt and higher PAR of 175–300 µmol photons m^-2 s^-1, showing an acclimatory offset in conditions favouring Biovolume- vs. Chl-specific growth rates (Fig 1).

Highest Chl-specific growth rates of NIES981 (full marine, East China Sea) were achieved across salinities of 0–32 ppt, but only at low light of 30 µmol photons m^-2 s^-1; in contrast Biovolume-specific growth rates for NIES981 were highest around salinity of 25 ppt but extended up to higher PAR of 100 µmol photons m^-2 s^-1 (Fig 1).

Chl-specific growth rates of CCMP1333 (full marine, Long Island Sound) were highest around salinities of 15–35 ppt, at low light of 30–100 µmol photons m^-2 s^-1, but the highest Biovolume-specific growth rates were offset to salinities around 30–35 ppt and higher light of 150–300 µmol photons m^-2 s^-1 (Fig 1).

Chl-specific growth rates of CCMP836 (full marine, Gulf of Mexico) were highest around salinities of 25–35 ppt, at PAR of 30–100 µmol photons m^-2 s^-1 (Fig 1) while the highest Biovolume-specific growth rates were achieved above salinity of 32 ppt, across the measured PAR from 30–100 µmol photons m^-2 s^-1 (Fig 1). Thus, again for CCMP836, conditions for the highest Biovolume-specific growth shift upwards to higher salinities and light, compared to Chl-specific growth.

As an alternative to strain-specific maximum growth rates (Fig 1), Fig 2 instead compares which strain achieves the highest Chl-specific or Biovolume-specific growth rate under a given combination of PAR and salinity.

CZS48M (brackish, Baltic Sea) is a Chl-specific growth winner across the range of salinities under high PAR, but is outcompeted by other strains at lower PAR, particularly as salinity increases. In contrast, CZS48M only achieves the highest Biovolume-specific growth under salinities less than ~ 20 ppt, but across the full range of PAR (Fig 2).

CZS25K (brackish, Baltic Sea) is only a winner for Chl-specific growth at salinities around 8 ppt, up to ~ 250 µmol photons m^-2 s^-1, but never achieves the highest Biovolume-specific growth rates among strains (Fig 2).

NIES981 (full marine, East China Sea) achieves the highest Chl-specific growth in a small region of moderate (~ 10 ppt) salinity and PAR below 30 µmol photons m^-2 s^-1, but never achieves the highest Biovolume-specific growth rate among strains (Fig 2).

CCMP1333 (full marine, Long Island Sound) achieves the highest Chl-specific growth at salinities ~15–25 ppt, and PAR below 150 µmol photons m^-2 s^-1 (Fig 2). CCMP1333 achieves the highest Biovolume-specific growth rates in a smaller region centred at salinity of ~ 22 ppt, below 100 µmol photons m^-2 s^-1 (Fig 2).

CCMP836 (full marine, Gulf of Mexico) achieved the highest Chl-specific growth above salinities of ~30 ppt, almost across the range of PAR. Similarly, CCMP836 achieved the highest Biovolume-specific growth rates across a wider range of salinities above ~ 20 ppt, again, from low to high PAR (Fig 2).

Interactions of photochemistry and metabolism, across salinity and light ranges

Each panel shows growth results from a different strain, ordered top to bottom, from low to high salinity tolerance. Red regions show the combinations of conditions where a strain achieved the top 10% of its F_V/F_M; while green regions show the conditions where a strain achieved the top 10% of its (1/ 𝜏) (s^-1). F_V/F_M and 1/ 𝜏 used for Generalized Additive Modelling were taken from fits of Fast Repetition and Relaxation fluorescence induction curves measured under the respective combinations of PAR and salinity for each strain (Fig 3). F_V/F_M, our proxy for photochemical potential, was, as expected, maximal for all strains under low PAR, but showed a shift towards higher salinities for the full marine strains (Fig 3).

Download:

Fig 3. Comparison of Conditions supporting Maximum PSII Photochemistry (F_V/F_M) or downstream electron transport rates (1/𝜏) (s^-1) of picocyanobacterial strains, growing across a matrix of salinities (ppt) and light levels (PAR, µmol photons m^-2 s^-1, (µE)).

https://doi.org/10.1371/journal.pone.0335835.g003

For all strains except CCMP836 (full marine, Gulf of Mexico) 1/𝜏_PSII, our proxy for metabolic capacity, showed a disjunct distribution of maximum values, achieved at both high, and low, PAR (Fig 3). Each strain nevertheless showed a distinct pattern of 1/𝜏_PSII, across the six salinity levels and three PAR (Fig 3). In general, CCMP836 showed high 1/𝜏_PSII compared to other strains under all conditions except low salinity (Fig 3). 1/𝜏_PSII of CCMP836 were highest above 30 ppt at light of 200–300 µmol photons m^-2 s^-1 (Fig 3) and were thus achieved under conditions overlapping its highest Biovolume-specific growth rates (Fig 1), with this strain showing a strong coupling between potential to generate metabolic electrons and achieved growth rate. F_V/F_M values for CCMP836 were highest around salinities of 25–35 ppt, at PAR below 100 µmol photons m^-2 s^-1.

Similarly, NIES981 (full marine, East China Sea) achieved high 1/𝜏_PSII above 30 ppt (Fig 3), slightly offset from its conditions for maximum Biovolume-specific growth rates that peaked around 25 ppt (Fig 1), but NIES981 also shows high 1/𝜏_PSII under low salinities where its growth is slower. Likewise, F_V/F_M values for NIES-981 were achieved at salinities above 10 ppt, but again only at low PAR of 30 µmol photons m^-2 s^-1. 1/𝜏_PSII for CCMP1333 (full marine, Long Island Sound) were, surprisingly, highest at low salinity (Fig 3), below its conditions for maximum Biovolume-specific growth rates (Fig 1). In contrast, F_V/F_M values for CCMP1333 were highest around a salinity of 20 ppt, under low PAR of 30 µmol photons m^-2 s^-1. 1/𝜏_PSII for CZS25K (brackish, Baltic Sea) were highest around 10–18 ppt and low PAR (Fig 3), overlapping the salinity range, but not the higher PAR where it achieved maximal growth rates (Fig 1). F_V/F_M values for CZS25K were highest around 10–25 ppt, again under PAR below 100 µmol photons m^-2 s^-1. 1/𝜏_PSII for CZS48M (brackish, Baltic Sea) was highest around 10 ppt at light of 300 µmol photons m^-2 s^-1 (Fig 3), overlapping with its conditions for maximal growth rates (Fig 1), but 1/𝜏_PSII was again high for CZS48M at salinity of 30 and low PAR.. In contrast, F_V/F_M values for CZS48M were highest across the salinity range from 0–35, but under PAR below 100 µmol photons m^-2 s^-1.

To study if photochemical capacity (F_V/F_M) (Fig 4) or metabolic capacity (1/tau (s^-1) (Fig 5) explained patterns of Biovolume-specific growth across salinities, we ran linear regression analyses (S9 Table) with final media salinity as a co-variant, while pooling data across strains and growth PAR, after preliminary analyses led us to exclude these as co-variants. Since Chl a is itself a key component of phytoplankton acclimation, we focused on Biovolume-specific growth rates.

Download:

Fig 4. Biovolume-specific growth rates (d^-1) of picocyanobacterial strains (symbol colours) vs. F_V/F_M across a matrix of growth media salinity (ppt; columns) and growth light level (PAR, µmol photons m^-2 s^-1; symbol size).

https://doi.org/10.1371/journal.pone.0335835.g004

Download:

Fig 5. Biovolume-specific growth rates (d^-1) of picocyanobacteria strains (symbol colours) vs. 1/𝜏 (s^-1) across a matrix of growth media salinity (ppt; columns) and growth light level (PAR, µmol photons m^-2 s^-1; symbol size).

https://doi.org/10.1371/journal.pone.0335835.g005

Interestingly, growth PAR from 30 to 300 µmol photons m^-2 s^-1 had little influence on the achieved Biovolume-specific growth rates, while, as seen in the GAM, CCMP836 and CCMP1333 stand out as achieving the highest Biovolume-specific growth rates, along with high F_V/F_M (Fig 4) and 1/tau (s^-1) (Fig 5) when growing under full marine salinity. Across strains, F_V/F_M was the only significant positive predictor of growth rate under moderate salinity of ~ 10 ppt, but not under full marine salinity (Fig 4). Conversely, 1/tau (s^-1) was a significant positive predictor of growth only under full marine salinity, driven largely because of rapid growth and high 1/tau (s^-1) of CCMP836 and CCMP1333 under marine salinity of ~33 ppt (Fig 5).

Genomic analyses of salinity tolerance marker genes

Our results show differences in the growth and photosynthetic capacities among the strains, isolated from environments with differing salinities. The original habitat of isolation, however, does not necessarily determine salt tolerance, as observed for the model cyanobacterium Synechocystis sp. PCC 6803, isolated from freshwater, but which can resist up to twofold seawater salinity. Therefore, we checked the genome sequences for genes encoding proteins potentially involved in salt acclimation using sequence probes derived from genes encoding verified salt resistance proteins from different cyanobacteria and heterotrophic bacteria (see S4 and S10 Tables). All the investigated cyanobacteria use the ‘salt out’ strategy under saline conditions, through export of inorganic ions such as Na⁺ and Cl^-, while also accumulating compatible solutes. Our analyses of a panel of genes encoding ion transporters shows (Fig 6) all strains possess transporters that can potentially export Na⁺ and Cl^- out of the cell, and several transporters involved in K⁺ uptake, along with a single putative importer for Na⁺ uptake, with limited evidence for a shift from the brackish to the full marine strains. This finding is similar to most other cyanobacteria, with no clear differences in the genetic capabilities for ion transport found among strains of different salt tolerances [8].

Download:

Fig 6. Summary of genes encoding ion transporters for the picocyanobacteria in this study, faceted by their respective ion substrates and whether they import or export ions.

Strains are ordered from lower (top) to higher (bottom) salinity tolerance. Symbol size represents counts of genes encoding each ion transporter in each genome. Colour represents log10 of the average E-value of the BLAST match between the probe sequence and the target sequence in the cyanobacterial genome. Symbol absence means no sequences encoding a given function were detected in a given genome.

https://doi.org/10.1371/journal.pone.0335835.g006

Another picture emerged when we searched for genes encoding proteins potentially involved in the biosynthesis of different compatible solutes (Fig 7). The full marine strain CCMP836 shows the most complete panel of genes encoding proteins synthesizing a full range of compatible solutes, including glycosyl glycerol, glucosylglycerate, sucrose, trehalose, and apparently glycine betaine. Interestingly CCMP836 has, however, a smaller total count of genes encoding both ion transporters (Fig 6) and compatible solutes (Fig 7), compared to the strains NIES981 and CCMP836. The strains NIES981 and CCMP836 show a modest drop in the count of genes encoding glycosyl-transferases and sucrose metabolism, suggestive of a shift away from carbohydrate compatible solutes towards glycine betaine (Fig 7). In contrast, the brackish water strains CZM48M and CZS25K can apparently not synthesize glycosyl glycerol, because the glycosyl glycerol-phosphate synthase is missing from their genomes.

Download:

Fig 7. Summary of genes encoding compatible solute biosynthesis in Cyanobacteria, faceted by solute products.

Strains are ordered from lower (top) to higher (bottom) salinity tolerance. Symbol size represents counts of genes encoding each ion transporter in each genome. Color represents log10 of the average E-value of the BLAST match between the probe sequence and the target sequence in the cyanobacterial genome. Symbol absence means no sequences encoding a given function were detected in a given genome.

https://doi.org/10.1371/journal.pone.0335835.g007

Discussion

We sought to test if picocyanobacterial strains isolated from environments with different salinities show distinct growth and physiological optima along a salinity gradient, and indeed found distinct growth, physiological salinity and PAR optima for each strain. Similarly, distinct growth niches were also described for other cyanobacteria from brackish environments worldwide [6,53]. However, no single physiological parameter explained the strain origins nor the strain growth responses to salinity and light, suggesting differential acclimation capacities across strains depending on environmental conditions.

For all strains, except the brackish CSZ48M, the conditions supporting the highest Biovolume-specific growth rates shift upwards towards higher salinities and PAR, when compared to Chl-specific growth rates (Fig 1). Interestingly, only the full marine strain NIES981 showed highest biovolume specific growth rates at lower irradiances compared to the other strains. These findings are consistent with another study where Synechococcus 7002 increased its biovolume by 300% under ultrafast growth at high irradiance levels [54]. Thus, increased biovolume growth rates might also suggest optimum growth conditions for single-cells and not necessarily colony-formation, as has sometimes been observed in the natural habitats of those strains [55,56].

Although Chl is a generally used proxy for biomass in phytoplankton biology, Chl/cell is itself a key component of acclimation [57], including responses to salinity in some phytoplankters [58]. Therefore, the apparent optimal niche for a strain will vary depending upon the metric used to track growth.

Across the tested strains the maxima for 1/𝜏_PSII, our proxy for metabolic use of electrons, are often offset from growth rate optima, consistent with varying ratios for the conversion of metabolic electrons to biomass, depending upon strain and condition, as indeed found in wider field studies [59]. We find, based upon maximal growth rates, that CZS48M and CZS25K, originating from brackish lagoons of the Baltic Sea, may be true ‘brackobionts,’ optimized for growth at brackish salinities and high PAR. Both of these strains show regions of maximal 1/𝜏_PSII coinciding with their conditions for maximal growth rates, but both also show high 1/𝜏_PSII under higher salinity and low PAR, suggesting a situation of rapid metabolism, but lower achieved growth, under stress at the edges of their environmental tolerance ranges. Conversely, NIES981, originating from fully marine habitat of the East China Sea, shows high 1/𝜏_PSII under high salinities, but also under low salinities, again suggesting conditions of rapid metabolism under stress at the lower end of its salinity tolerance. Thus, overall, 1/𝜏_PSII shows suggestive interactions with patterns of growth rates but is a poor generic predictor of achieved growth under stressed conditions.

In our study CCMP1333 (WH5701) shows highest Chl and biovolume growth rates at intermediate salinities, before trailing off at the highest salinities, similar to recent findings [60]. In addition, a similar decrease at the highest salinities was detected for F_V/F_M for CCMP1333, suggesting an optimum niche somewhat below full salinity marine water. The highest biovolume growth rates for CCMP1333 at intermediate salinities, point to more biomass production, and hence elemental demand, compared to other salinities and strains. The CCMP1333 elemental demand for nitrogen similarly drops with increasing salinities, peaking below salinities of 36 ppt based on transcriptomic studies [60]. In contrast, phosphorus demand by CCMP1333 peaks at salinities above 36 ppt [61], possibly reflecting stress, with phosphate incorporated as PolyP to function as energy buffer [27,62].

As expected [8] the picocyanobacteria showed no clear differences in their genetic capabilities for ion transport based upon the salinities of the originating habitats (Fig 6) and salinity tolerances. In contrast their genomic complements for genes encoding proteins involved in the biosynthesis of different compatible solutes did vary systematically across strains, with the full marine strain CCMP836 showing the most complete panel of genes encoding compatible solute synthesis, whereas the brackish water strains CZM48M and CZS25K lack the gene encoding glycosyl glycerol-phosphate synthase (Fig 7). In all strains, genes for the synthesis of sucrose [63] and trehalose synthesis are present, which permit the brackish water strains CZM48M and CZS25K to resist moderate salinities up to 30 ppt. On the other hand, CCMP1333, and NIES981 possess up to ten times more genes encoding transposases, compared to other marine picocyanobacterial strains [61] and are thus potentially more able to adapt genomically to new salinity levels, possibly explaining their wide growth ranges found in this study. Furthermore, some picocyanobacteria react to lowered salinities with production of high light proteins, and a simultaneous drop in production of osmolytes [64]. Thus, several growth optima across a wide range of light and salinity are possible, with trade-offs in a fluctuating environment. Hence the different growth and physiological responses towards increasing salinities across strains are explicable mainly through the differential acquisition or losses of genes to synthesize appropriate compatible solutes.

Optical attenuation in the water column is another metric of competitive advantage among strains, since the success of picocyanobacteria in lagoon environments may relate to their high achieved optical attenuation, relative to biomass and pigment content [65]. Growth rates of optical attenuation (data not presented), a close correlate of Chl-specific growth rates, might therefore allow a picocyanobacteria community to reach and then maintain dominance through shading of other phytoplankton and macrophytes, particularly since the brackish cyanobacteria show only limited growth rate responses to changing PAR. Salinity may itself alter bio-optical attenuation through several mechanisms, including induction of increased cyanobacterial production of exopolysaccharides, leading to cell clumping and increased attenuation [56]. Another mechanism is change in pigmentation under different salinities [66], thus also allowing growth across a wider salinity range.

Conclusions

We showed that all five tested strains, even though closely related and with similar phenotypes, showed different growth strategies with respect to salinity. Thus, the inter-strain diversity of these picocyanobacteria allow a number of strains to collectively dominate across a wide environmental range. This might contribute to potential picocyanobacterial dominance in future coastal seas with increased temperatures, light attenuation and nutrient availability. The capacities of these strains, with their related ecological traits, may lead to a picocyanobacterial dominance of even more ecosystems in the future, ultimately leading to long-term food web degradation [67]. It is therefore necessary to include microbial strain diversity in coastal water monitoring and management decisions, as this diversity might act as a buffer against the success of potential restoration measures.

Supporting information

S1 Table. Strain specific estimates to convert OD₆₈₀-OD₇₅₀ to μg chl mL^-1 determined with a Turner fluorometer.

https://doi.org/10.1371/journal.pone.0335835.s001

(CSV)

S2 Table. Strain-specific Biovolume estimates (μm³) from microscopy.

https://doi.org/10.1371/journal.pone.0335835.s002

(CSV)

S3 Table. Gompertz Growth models were fit separately for each strain, salinity and plate replicate (n = 3) based on the pooled four (start of experiment) to two (end of experiment) technical replicate wells per plate for each salinity.

https://doi.org/10.1371/journal.pone.0335835.s003

(CSV)

S4 Table. Genes probes used to search genomes.

https://doi.org/10.1371/journal.pone.0335835.s004

(CSV)

S5 Table. Generalized additive models for chlorophyll specific growth of each strain, with salinity and light level as explanatory variables.

https://doi.org/10.1371/journal.pone.0335835.s005

(CSV)

S6 Table. Generalized additive models for biovolume specific growth of each strain, with salinity and light level as explanatory variables.

https://doi.org/10.1371/journal.pone.0335835.s006

(CSV)

S7 Table. Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) for each strain, with salinity and light level as explanatory variables.

https://doi.org/10.1371/journal.pone.0335835.s007

(CSV)

S8 Table. Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) for each strain, with salinity and light level as explanatory variables.

https://doi.org/10.1371/journal.pone.0335835.s008

(CSV)

S9 Table. Linear regression results for Biovolume-specific growth rates (d^-1) with respect to F_V/F_M or 1/tau (s^-1), with covariant growth media salinity (ppt), data pooled across strains and growth lights.

https://doi.org/10.1371/journal.pone.0335835.s009

(CSV)

S10 Table. Summary of genes in studied picocyanobacteria, encoding proteins potentially involved in salt acclimation, detected using sequence probes derived from verified salt resistance proteins from different cyanobacteria and heterotrophic bacteria.

https://doi.org/10.1371/journal.pone.0335835.s010

(CSV)

S11 Fig. Generalized additive models of CZS48M for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

Generalized additive models for photophysiological parameters FV/FM and metabolic capacity 1/tau (s-1) (E & F) across salinity and light levels.

https://doi.org/10.1371/journal.pone.0335835.s011

(JPEG)

S12 Fig. Generalized additive models of CZS25K for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) (E & F) across salinity and light levels.

https://doi.org/10.1371/journal.pone.0335835.s012

(JPEG)

S13 Fig. Generalized additive models of CCMP1333 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) (E & F) across salinity and light levels.

https://doi.org/10.1371/journal.pone.0335835.s013

(JPEG)

S14 Fig. Generalized additive models of NIES981 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) (E & F) across salinity and light levels.

https://doi.org/10.1371/journal.pone.0335835.s014

(JPEG)

S15 Fig. Generalized additive models of CCMP836 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) (E & F) across salinity and light levels.

https://doi.org/10.1371/journal.pone.0335835.s015

(JPEG)

References

1. Hugerth LW, Larsson J, Alneberg J, Lindh MV, Legrand C, Pinhassi J, et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 2015;16:279. pmid:26667648
- View Article
- PubMed/NCBI
- Google Scholar
2. Badger MR, Hanson D, Price GD. Evolution and diversity of CO2 concentrating mechanisms in cyanobacteria. Funct Plant Biol. 2002;29(3):161–73. pmid:32689463
- View Article
- PubMed/NCBI
- Google Scholar
3. Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci U S A. 2013;110(24):9824–9. pmid:23703908
- View Article
- PubMed/NCBI
- Google Scholar
4. Herdman M, Castenholz RW, Waterbury JB, Rippka R. Form- Synechococcus. In: Whitman WB, Rainey F, Kämpfer P, Trujillo M, Chun J, DeVos P, editors. Bergey’s Manual of Systematics of Archaea and Bacteria. Chichester, UK: John Wiley & Sons, Ltd. 2015. p. 1–7. https://doi.org/10.1002/9781118960608.gbm00422
5. Xia X, Vidyarathna NK, Palenik B, Lee P, Liu H. Comparison of the seasonal variations of Synechococcus assemblage structures in estuarine waters and coastal waters of Hong Kong. Appl Environ Microbiol. 2015;81(21):7644–55. pmid:26319880
- View Article
- PubMed/NCBI
- Google Scholar
6. Aguilera A, Alegria Zufia J, Bas Conn L, Gurlit L, Śliwińska-Wilczewska S, Budzałek G, et al. Ecophysiological analysis reveals distinct environmental preferences in closely related Baltic Sea picocyanobacteria. Environ Microbiol. 2023;25(9):1674–95. pmid:37655642
- View Article
- PubMed/NCBI
- Google Scholar
7. Celepli N, Sundh J, Ekman M, Dupont CL, Yooseph S, Bergman B, et al. Meta-omic analyses of Baltic Sea cyanobacteria: Diversity, community structure and salt acclimation. Environ Microbiol. 2017;19(2):673–86. pmid:27871145
- View Article
- PubMed/NCBI
- Google Scholar
8. Hagemann M. Molecular biology of cyanobacterial salt acclimation. FEMS Microbiol Rev. 2011;35(1):87–123. pmid:20618868
- View Article
- PubMed/NCBI
- Google Scholar
9. Reed RH, Borowitzka LJ, Mackay MA, Chudek JA, Foster R, Warr SRC, et al. Organic solute accumulation in osmotically stressed cyanobacteria. FEMS Microbiology Letters. 1986;39(1–2):51–6.
- View Article
- Google Scholar
10. Pade N, Michalik D, Ruth W, Belkin N, Hess WR, Berman-Frank I, et al. Trimethylated homoserine functions as the major compatible solute in the globally significant oceanic cyanobacterium Trichodesmium. Proc Natl Acad Sci USA. 2016;113(46):13191–6. pmid:27799527
- View Article
- PubMed/NCBI
- Google Scholar
11. Bianchini G, Hagemann M, Sánchez-Baracaldo P. Stochastic character mapping, bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria. Syst Biol. 2024;73(4):644–65. pmid:38934241
- View Article
- PubMed/NCBI
- Google Scholar
12. Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249–99.
- View Article
- Google Scholar
13. Shibl AA, Ngugi DK, Talarmin A, Thompson LR, Blom J, Stingl U. The genome of a novel isolate of Prochlorococcus from the Red Sea contains transcribed genes for compatible solute biosynthesis. FEMS Microbiol Ecol. 2018;94(12). pmid:30188995
- View Article
- PubMed/NCBI
- Google Scholar
14. Klähn S, Steglich C, Hess WR, Hagemann M. Glucosylglycerate: a secondary compatible solute common to marine cyanobacteria from nitrogen-poor environments. Environ Microbiol. 2010;12(1):83–94. pmid:19735283
- View Article
- PubMed/NCBI
- Google Scholar
15. Godhe A, Rynearson T. The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments. Philos Trans R Soc Lond B Biol Sci. 2017;372(1728):20160399. pmid:28717025
- View Article
- PubMed/NCBI
- Google Scholar
16. Daufresne M, Lengfellner K, Sommer U. Global warming benefits the small in aquatic ecosystems. Proc Natl Acad Sci U S A. 2009;106(31):12788–93. pmid:19620720
- View Article
- PubMed/NCBI
- Google Scholar
17. Peter KH, Sommer U. Phytoplankton cell size reduction in response to warming mediated by nutrient limitation. PLoS One. 2013;8(9):e71528. pmid:24039717
- View Article
- PubMed/NCBI
- Google Scholar
18. Sommer U, Peter KH, Genitsaris S, Moustaka-Gouni M. Do marine phytoplankton follow Bergmann’s rule sensu lato? Biol Rev Camb Philos Soc. 2017;92(2):1011–26. pmid:27028628
- View Article
- PubMed/NCBI
- Google Scholar
19. Lürling M. Grazing resistance in phytoplankton. Hydrobiologia. 2020;848(1):237–49.
- View Article
- Google Scholar
20. Roselli L, Basset A. Decoding size distribution patterns in marine and transitional water phytoplankton: From community to species level. PLoS One. 2015;10(5):e0127193. pmid:25974052
- View Article
- PubMed/NCBI
- Google Scholar
21. Scheffer M, Rinaldi S, Gragnani A, Mur LR, van Nes EH. On the dominance of filamentous cyanobacteria in shallow, turbid lakes. Ecology. 1997;78(1):272–82.
- View Article
- Google Scholar
22. Kemp AES, Villareal TA. The case of the diatoms and the muddled mandalas: time to recognize diatom adaptations to stratified waters. Prog Oceanogr. 2018;167:138–49.
- View Article
- Google Scholar
23. Liénart C, Garbaras A, Qvarfordt S, Sysoev AÖ, Höglander H, Walve J. Long-term changes in trophic ecology of blue mussels in a rapidly changing ecosystem. Limnol Oceanogr. 2020;:1–17. https://doi.org/10.1002/lno.11633
24. Fuller NJ, Marie D, Partensky F, Vaulot D, Post AF, Scanlan DJ. Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red Sea. Appl Environ Microbiol. 2003;69(5):2430–43. pmid:12732508
- View Article
- PubMed/NCBI
- Google Scholar
25. Dufresne A, Ostrowski M, Scanlan DJ, Garczarek L, Mazard S, Palenik BP, et al. Unraveling the genomic mosaic of a ubiquitous genus of marine cyanobacteria. Genome Biol. 2008;9(5):R90. pmid:18507822
- View Article
- PubMed/NCBI
- Google Scholar
26. Six C, Thomas J-C, Garczarek L, Ostrowski M, Dufresne A, Blot N, et al. Diversity and evolution of phycobilisomes in marine Synechococcus spp.: A comparative genomics study. Genome Biol. 2007;8(12):R259. pmid:18062815
- View Article
- PubMed/NCBI
- Google Scholar
27. Berthold M, Campbell DA. Restoration, conservation and phytoplankton hysteresis. Conserv Physiol. 2021;9(1):coab062. pmid:34394942
- View Article
- PubMed/NCBI
- Google Scholar
28. Chen F. Picocyanobacteria in the Chesapeake Bay: Isolation, diversity, and adaptation. Mar Life Sci Technol. 2025;7(3):434–49. pmid:40919476
- View Article
- PubMed/NCBI
- Google Scholar
29. Suzuki S, Yamaguchi H, Kawachi M. Potential genetic diversity of Synechococcus-related strains maintained in the microbial culture collection of the national institute for environmental studies, Japan. 2022;38.
30. Allen MM, Stanier RY. Growth and division of some unicellular blue-green algae. J Gen Microbiol. 1968;51(2):199–202. pmid:5652095
- View Article
- PubMed/NCBI
- Google Scholar
31. Watanabe MM, Kawachi M, Hiroki M, Kasai F. NIES Collection List of Strains. Sixth Edition, 2000, Microalgae and Protozoa. Sixth Edition. Tsukuba, Japan: National Institute for Environmental Studies. 2000.
32. Collier JL, Lovindeer R, Xi Y, Radway JC, Armstrong RA. Differences in growth and physiology of marine synechococcus (cyanobacteria) on nitrate versus ammonium are not determined solely by nitrogen source redox state(1). J Phycol. 2012;48(1):106–16. pmid:27009655
- View Article
- PubMed/NCBI
- Google Scholar
33. Gattuso JP, Epitalon JM, Lavigne H, Orr J. Seawater carbonate chemistry. 2024.Available from: https://CRAN.R-project.org/package=seacarb
34. Berthold M, Albrecht M, Campbell DA, Omar NM. Draft genomes of 3 cyanobacteria strains and 17 co-habiting proteobacteria assembled from metagenomes. Microbiol Resour Announc. 2023;12(12):e0046023. pmid:37943043
- View Article
- PubMed/NCBI
- Google Scholar
35. Yamaguchi H, Shimura Y, Suzuki S, Yamagishi T, Tatarazako N, Kawachi M. Complete genome sequence of Cyanobium sp. NIES-981, a marine strain potentially useful for ecotoxicological bioassays. Genome Announcements. 2016;4:e00736–16.
- View Article
- Google Scholar
36. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2023.
37. Posit team. RStudio: Integrated development environment for R. Boston, MA: Posit Software, PBC. 2025.
38. Xie Y. knitr: A Comprehensive Tool for Reproducible Research in R. Implementing Reproducible Research. Chapman and Hall/CRC. 2018. p. 3–31. https://doi.org/10.1201/9781315373461-1
39. Elzhov TV, Mullen KM, Spiess AN, Bolker B. minpack.lm: R interface to the levenberg-marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. 2016. Available from: https://CRAN.R-project.org/package=minpack.lm
40. von Jouanne-Diedrich H. OneR: One rule machine learning classification algorithm with enhancements. 2017. https://github.com/vonjd/OneR
41. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686.
- View Article
- Google Scholar
42. Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C. ggplot2: Create elegant data visualisations using the grammar of graphics. 2023. Available from: https://CRAN.R-project.org/package=ggplot2
43. Zhu H. kableExtra: Construct complex table with kable and pipe syntax. 2021. https://CRAN.R-project.org/package=kableExtra
44. Wickham H, Seidel D. Scales: Scale functions for visualization. 2022. https://CRAN.R-project.org/package=scales
45. Robinson D, Hayes A, Couch S. broom: Convert statistical objects into tidy tibbles. 2023. https://CRAN.R-project.org/package=broom
46. Tjørve KMC, Tjørve E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One. 2017;12(6):e0178691. pmid:28582419
- View Article
- PubMed/NCBI
- Google Scholar
47. Zwietering MH, Jongenburger I, Rombouts FM, van ’t Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990;56(6):1875–81. pmid:16348228
- View Article
- PubMed/NCBI
- Google Scholar
48. Kolber Z, Prasil O, Falkowski P. Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: Defining methodology and experimental protocols. Biochim Biophys Acta. 1998;1367(1–3):88–106. pmid:9784616
- View Article
- PubMed/NCBI
- Google Scholar
49. Xu K, Grant-Burt JL, Donaher N, Campbell DA. Connectivity among Photosystem II centers in phytoplankters: Patterns and responses. Biochim Biophys Acta Bioenerg. 2017;1858(6):459–74. pmid:28315315
- View Article
- PubMed/NCBI
- Google Scholar
50. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: Architecture and applications. BMC Bioinformatics. 2009;10:421. pmid:20003500
- View Article
- PubMed/NCBI
- Google Scholar
51. Wood SN. Generalized additive models: an introduction with R. 2nd ed. Chapman and Hall/CRC. 2017.
52. Wood S. mgcv: Mixed GAM computation vehicle with automatic smoothness estimation. 2023. Available from: https://CRAN.R-project.org/package=mgcv
53. Kim Y, Jeon J, Kwak MS, Kim GH, Koh I, Rho M. Photosynthetic functions of Synechococcus in the ocean microbiomes of diverse salinity and seasons. PLoS One. 2018;13(1):e0190266. pmid:29293601
- View Article
- PubMed/NCBI
- Google Scholar
54. Bernstein HC, McClure RS, Hill EA, Markillie LM, Chrisler WB, Romine MF, et al. Unlocking the constraints of cyanobacterial productivity: acclimations enabling ultrafast growth. mBio. 2016;7(4):e00949–16. pmid:27460798
- View Article
- PubMed/NCBI
- Google Scholar
55. Albrecht M, Pröschold T, Schumann R. Identification of Cyanobacteria in a Eutrophic Coastal Lagoon on the Southern Baltic Coast. Front Microbiol. 2017;8:923. pmid:28611738
- View Article
- PubMed/NCBI
- Google Scholar
56. Bemal S, Anil AC. Effects of salinity on cellular growth and exopolysaccharide production of freshwater Synechococcus strain CCAP1405. J Plankton Res. 2017;40(1):46–58.
- View Article
- Google Scholar
57. Geider RJ. Light and temperature dependence of the carbon to chlorophyll a ratio in microalgae and cyanobacteria: Implications for physiology and growth of phytoplankton. New Phytologist. 1987;106(1):1–34.
- View Article
- Google Scholar
58. McLachlan J. The effect of salinity on growth and chlorophyll content in representative classes of unicellular marine algae. Can J Microbiol. 1961;7:399–406.
- View Article
- Google Scholar
59. Hughes DJ, Campbell DA, Doblin MA, Kromkamp JC, Lawrenz E, Moore CM, et al. Roadmaps and detours: Active chlorophyll- a assessments of primary productivity across marine and freshwater systems. Environ Sci Technol. 2018;52(21):12039–54. pmid:30247887
- View Article
- PubMed/NCBI
- Google Scholar
60. Escribano-Gómez I, Pérez R, Arrizabalaga U, Liébana R, Vergara-Len M, López-Urrutia Á, et al. Synechococcus under stress: contrasting physiological and transcriptional responses to salinity and temperature in marine versus euryhaline strains. Environ Microbiol Rep. 2026;18(1):e70273. pmid:41587387
- View Article
- PubMed/NCBI
- Google Scholar
61. Cabello-Yeves PJ, Callieri C, Picazo A, Schallenberg L, Huber P, Roda-Garcia JJ, et al. Elucidating the picocyanobacteria salinity divide through ecogenomics of new freshwater isolates. BMC Biol. 2022;20(1):175. pmid:35941649
- View Article
- PubMed/NCBI
- Google Scholar
62. Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA. Phytoplankton in a changing world: Cell size and elemental stoichiometry. J Plankton Res. 2009;32(1):119–37.
- View Article
- Google Scholar
63. Liang Y, Zhang M, Wang M, Zhang W, Qiao C, Luo Q, et al. Freshwater cyanobacterium synechococcus elongatus PCC 7942 adapts to an environment with salt stress via ion-induced enzymatic balance of compatible solutes. Appl Environ Microbiol. 2020;86(7):e02904-19. pmid:31953341
- View Article
- PubMed/NCBI
- Google Scholar
64. Xia X, Liao Y, Liu J, Leung SK, Lee PY, Zhang L, et al. Genomic and transcriptomic insights into salinity tolerance-based niche differentiation of synechococcus clades in estuarine and coastal waters. mSystems. 2023;8(1):e0110622. pmid:36622156
- View Article
- PubMed/NCBI
- Google Scholar
65. Berthold M, Schumann R. Phosphorus dynamics in a eutrophic lagoon: uptake and utilization of nutrient pulses by phytoplankton. Front Mar Sci. 2020;7:281.
- View Article
- Google Scholar
66. Schubert H, Fulda S, Hagemann M. Effects of adaptation to different salt concentrations on photosynthesis and pigmentation of the cyanobacterium synechocystis sp. PCC 6803. Journal of Plant Physiology. 1993;142(3):291–5.
- View Article
- Google Scholar
67. Schmidt K, Birchill AJ, Atkinson A, Brewin RJW, Clark JR, Hickman AE, et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob Chang Biol. 2020;26(10):5574–87. pmid:32506810
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Hugerth LW, Larsson J, Alneberg J, Lindh MV, Legrand C, Pinhassi J, et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 2015;16:279. pmid:26667648
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Badger MR, Hanson D, Price GD. Evolution and diversity of CO2 concentrating mechanisms in cyanobacteria. Funct Plant Biol. 2002;29(3):161–73. pmid:32689463
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Flombaum P, Gallegos JL, Gordillo RA, Rincón J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci U S A. 2013;110(24):9824–9. pmid:23703908
View Article
PubMed/NCBI
Google Scholar

[10] View Article

[11] PubMed/NCBI

[12] Google Scholar

[ref4] 4. Herdman M, Castenholz RW, Waterbury JB, Rippka R. Form- Synechococcus. In: Whitman WB, Rainey F, Kämpfer P, Trujillo M, Chun J, DeVos P, editors. Bergey’s Manual of Systematics of Archaea and Bacteria. Chichester, UK: John Wiley & Sons, Ltd. 2015. p. 1–7. https://doi.org/10.1002/9781118960608.gbm00422

[ref5] 5. Xia X, Vidyarathna NK, Palenik B, Lee P, Liu H. Comparison of the seasonal variations of Synechococcus assemblage structures in estuarine waters and coastal waters of Hong Kong. Appl Environ Microbiol. 2015;81(21):7644–55. pmid:26319880
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref6] 6. Aguilera A, Alegria Zufia J, Bas Conn L, Gurlit L, Śliwińska-Wilczewska S, Budzałek G, et al. Ecophysiological analysis reveals distinct environmental preferences in closely related Baltic Sea picocyanobacteria. Environ Microbiol. 2023;25(9):1674–95. pmid:37655642
View Article
PubMed/NCBI
Google Scholar

[19] View Article

[20] PubMed/NCBI

[21] Google Scholar

[ref7] 7. Celepli N, Sundh J, Ekman M, Dupont CL, Yooseph S, Bergman B, et al. Meta-omic analyses of Baltic Sea cyanobacteria: Diversity, community structure and salt acclimation. Environ Microbiol. 2017;19(2):673–86. pmid:27871145
View Article
PubMed/NCBI
Google Scholar

[23] View Article

[24] PubMed/NCBI

[25] Google Scholar

[ref8] 8. Hagemann M. Molecular biology of cyanobacterial salt acclimation. FEMS Microbiol Rev. 2011;35(1):87–123. pmid:20618868
View Article
PubMed/NCBI
Google Scholar

[27] View Article

[28] PubMed/NCBI

[29] Google Scholar

[ref9] 9. Reed RH, Borowitzka LJ, Mackay MA, Chudek JA, Foster R, Warr SRC, et al. Organic solute accumulation in osmotically stressed cyanobacteria. FEMS Microbiology Letters. 1986;39(1–2):51–6.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref10] 10. Pade N, Michalik D, Ruth W, Belkin N, Hess WR, Berman-Frank I, et al. Trimethylated homoserine functions as the major compatible solute in the globally significant oceanic cyanobacterium Trichodesmium. Proc Natl Acad Sci USA. 2016;113(46):13191–6. pmid:27799527
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref11] 11. Bianchini G, Hagemann M, Sánchez-Baracaldo P. Stochastic character mapping, bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria. Syst Biol. 2024;73(4):644–65. pmid:38934241
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref12] 12. Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249–99.
View Article
Google Scholar

[42] View Article

[43] Google Scholar

[ref13] 13. Shibl AA, Ngugi DK, Talarmin A, Thompson LR, Blom J, Stingl U. The genome of a novel isolate of Prochlorococcus from the Red Sea contains transcribed genes for compatible solute biosynthesis. FEMS Microbiol Ecol. 2018;94(12). pmid:30188995
View Article
PubMed/NCBI
Google Scholar

[45] View Article

[46] PubMed/NCBI

[47] Google Scholar

[ref14] 14. Klähn S, Steglich C, Hess WR, Hagemann M. Glucosylglycerate: a secondary compatible solute common to marine cyanobacteria from nitrogen-poor environments. Environ Microbiol. 2010;12(1):83–94. pmid:19735283
View Article
PubMed/NCBI
Google Scholar

[49] View Article

[50] PubMed/NCBI

[51] Google Scholar

[ref15] 15. Godhe A, Rynearson T. The role of intraspecific variation in the ecological and evolutionary success of diatoms in changing environments. Philos Trans R Soc Lond B Biol Sci. 2017;372(1728):20160399. pmid:28717025
View Article
PubMed/NCBI
Google Scholar

[53] View Article

[54] PubMed/NCBI

[55] Google Scholar

[ref16] 16. Daufresne M, Lengfellner K, Sommer U. Global warming benefits the small in aquatic ecosystems. Proc Natl Acad Sci U S A. 2009;106(31):12788–93. pmid:19620720
View Article
PubMed/NCBI
Google Scholar

[57] View Article

[58] PubMed/NCBI

[59] Google Scholar

[ref17] 17. Peter KH, Sommer U. Phytoplankton cell size reduction in response to warming mediated by nutrient limitation. PLoS One. 2013;8(9):e71528. pmid:24039717
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref18] 18. Sommer U, Peter KH, Genitsaris S, Moustaka-Gouni M. Do marine phytoplankton follow Bergmann’s rule sensu lato? Biol Rev Camb Philos Soc. 2017;92(2):1011–26. pmid:27028628
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref19] 19. Lürling M. Grazing resistance in phytoplankton. Hydrobiologia. 2020;848(1):237–49.
View Article
Google Scholar

[69] View Article

[70] Google Scholar

[ref20] 20. Roselli L, Basset A. Decoding size distribution patterns in marine and transitional water phytoplankton: From community to species level. PLoS One. 2015;10(5):e0127193. pmid:25974052
View Article
PubMed/NCBI
Google Scholar

[72] View Article

[73] PubMed/NCBI

[74] Google Scholar

[ref21] 21. Scheffer M, Rinaldi S, Gragnani A, Mur LR, van Nes EH. On the dominance of filamentous cyanobacteria in shallow, turbid lakes. Ecology. 1997;78(1):272–82.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref22] 22. Kemp AES, Villareal TA. The case of the diatoms and the muddled mandalas: time to recognize diatom adaptations to stratified waters. Prog Oceanogr. 2018;167:138–49.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref23] 23. Liénart C, Garbaras A, Qvarfordt S, Sysoev AÖ, Höglander H, Walve J. Long-term changes in trophic ecology of blue mussels in a rapidly changing ecosystem. Limnol Oceanogr. 2020;:1–17. https://doi.org/10.1002/lno.11633

[ref24] 24. Fuller NJ, Marie D, Partensky F, Vaulot D, Post AF, Scanlan DJ. Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red Sea. Appl Environ Microbiol. 2003;69(5):2430–43. pmid:12732508
View Article
PubMed/NCBI
Google Scholar

[83] View Article

[84] PubMed/NCBI

[85] Google Scholar

[ref25] 25. Dufresne A, Ostrowski M, Scanlan DJ, Garczarek L, Mazard S, Palenik BP, et al. Unraveling the genomic mosaic of a ubiquitous genus of marine cyanobacteria. Genome Biol. 2008;9(5):R90. pmid:18507822
View Article
PubMed/NCBI
Google Scholar

[87] View Article

[88] PubMed/NCBI

[89] Google Scholar

[ref26] 26. Six C, Thomas J-C, Garczarek L, Ostrowski M, Dufresne A, Blot N, et al. Diversity and evolution of phycobilisomes in marine Synechococcus spp.: A comparative genomics study. Genome Biol. 2007;8(12):R259. pmid:18062815
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref27] 27. Berthold M, Campbell DA. Restoration, conservation and phytoplankton hysteresis. Conserv Physiol. 2021;9(1):coab062. pmid:34394942
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref28] 28. Chen F. Picocyanobacteria in the Chesapeake Bay: Isolation, diversity, and adaptation. Mar Life Sci Technol. 2025;7(3):434–49. pmid:40919476
View Article
PubMed/NCBI
Google Scholar

[99] View Article

[100] PubMed/NCBI

[101] Google Scholar

[ref29] 29. Suzuki S, Yamaguchi H, Kawachi M. Potential genetic diversity of Synechococcus-related strains maintained in the microbial culture collection of the national institute for environmental studies, Japan. 2022;38.

[ref30] 30. Allen MM, Stanier RY. Growth and division of some unicellular blue-green algae. J Gen Microbiol. 1968;51(2):199–202. pmid:5652095
View Article
PubMed/NCBI
Google Scholar

[104] View Article

[105] PubMed/NCBI

[106] Google Scholar

[ref31] 31. Watanabe MM, Kawachi M, Hiroki M, Kasai F. NIES Collection List of Strains. Sixth Edition, 2000, Microalgae and Protozoa. Sixth Edition. Tsukuba, Japan: National Institute for Environmental Studies. 2000.

[ref32] 32. Collier JL, Lovindeer R, Xi Y, Radway JC, Armstrong RA. Differences in growth and physiology of marine synechococcus (cyanobacteria) on nitrate versus ammonium are not determined solely by nitrogen source redox state(1). J Phycol. 2012;48(1):106–16. pmid:27009655
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref33] 33. Gattuso JP, Epitalon JM, Lavigne H, Orr J. Seawater carbonate chemistry. 2024.Available from: https://CRAN.R-project.org/package=seacarb

[ref34] 34. Berthold M, Albrecht M, Campbell DA, Omar NM. Draft genomes of 3 cyanobacteria strains and 17 co-habiting proteobacteria assembled from metagenomes. Microbiol Resour Announc. 2023;12(12):e0046023. pmid:37943043
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref35] 35. Yamaguchi H, Shimura Y, Suzuki S, Yamagishi T, Tatarazako N, Kawachi M. Complete genome sequence of Cyanobium sp. NIES-981, a marine strain potentially useful for ecotoxicological bioassays. Genome Announcements. 2016;4:e00736–16.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref36] 36. R Core Team. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2023.

[ref37] 37. Posit team. RStudio: Integrated development environment for R. Boston, MA: Posit Software, PBC. 2025.

[ref38] 38. Xie Y. knitr: A Comprehensive Tool for Reproducible Research in R. Implementing Reproducible Research. Chapman and Hall/CRC. 2018. p. 3–31. https://doi.org/10.1201/9781315373461-1

[ref39] 39. Elzhov TV, Mullen KM, Spiess AN, Bolker B. minpack.lm: R interface to the levenberg-marquardt nonlinear least-squares algorithm found in MINPACK, plus support for bounds. 2016. Available from: https://CRAN.R-project.org/package=minpack.lm

[ref40] 40. von Jouanne-Diedrich H. OneR: One rule machine learning classification algorithm with enhancements. 2017. https://github.com/vonjd/OneR

[ref41] 41. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686.
View Article
Google Scholar

[126] View Article

[127] Google Scholar

[ref42] 42. Wickham H, Chang W, Henry L, Pedersen TL, Takahashi K, Wilke C. ggplot2: Create elegant data visualisations using the grammar of graphics. 2023. Available from: https://CRAN.R-project.org/package=ggplot2

[ref43] 43. Zhu H. kableExtra: Construct complex table with kable and pipe syntax. 2021. https://CRAN.R-project.org/package=kableExtra

[ref44] 44. Wickham H, Seidel D. Scales: Scale functions for visualization. 2022. https://CRAN.R-project.org/package=scales

[ref45] 45. Robinson D, Hayes A, Couch S. broom: Convert statistical objects into tidy tibbles. 2023. https://CRAN.R-project.org/package=broom

[ref46] 46. Tjørve KMC, Tjørve E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS One. 2017;12(6):e0178691. pmid:28582419
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref47] 47. Zwietering MH, Jongenburger I, Rombouts FM, van ’t Riet K. Modeling of the bacterial growth curve. Appl Environ Microbiol. 1990;56(6):1875–81. pmid:16348228
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref48] 48. Kolber Z, Prasil O, Falkowski P. Measurements of variable chlorophyll fluorescence using fast repetition rate techniques: Defining methodology and experimental protocols. Biochim Biophys Acta. 1998;1367(1–3):88–106. pmid:9784616
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref49] 49. Xu K, Grant-Burt JL, Donaher N, Campbell DA. Connectivity among Photosystem II centers in phytoplankters: Patterns and responses. Biochim Biophys Acta Bioenerg. 2017;1858(6):459–74. pmid:28315315
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref50] 50. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: Architecture and applications. BMC Bioinformatics. 2009;10:421. pmid:20003500
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref51] 51. Wood SN. Generalized additive models: an introduction with R. 2nd ed. Chapman and Hall/CRC. 2017.

[ref52] 52. Wood S. mgcv: Mixed GAM computation vehicle with automatic smoothness estimation. 2023. Available from: https://CRAN.R-project.org/package=mgcv

[ref53] 53. Kim Y, Jeon J, Kwak MS, Kim GH, Koh I, Rho M. Photosynthetic functions of Synechococcus in the ocean microbiomes of diverse salinity and seasons. PLoS One. 2018;13(1):e0190266. pmid:29293601
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref54] 54. Bernstein HC, McClure RS, Hill EA, Markillie LM, Chrisler WB, Romine MF, et al. Unlocking the constraints of cyanobacterial productivity: acclimations enabling ultrafast growth. mBio. 2016;7(4):e00949–16. pmid:27460798
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref55] 55. Albrecht M, Pröschold T, Schumann R. Identification of Cyanobacteria in a Eutrophic Coastal Lagoon on the Southern Baltic Coast. Front Microbiol. 2017;8:923. pmid:28611738
View Article
PubMed/NCBI
Google Scholar

[163] View Article

[164] PubMed/NCBI

[165] Google Scholar

[ref56] 56. Bemal S, Anil AC. Effects of salinity on cellular growth and exopolysaccharide production of freshwater Synechococcus strain CCAP1405. J Plankton Res. 2017;40(1):46–58.
View Article
Google Scholar

[167] View Article

[168] Google Scholar

[ref57] 57. Geider RJ. Light and temperature dependence of the carbon to chlorophyll a ratio in microalgae and cyanobacteria: Implications for physiology and growth of phytoplankton. New Phytologist. 1987;106(1):1–34.
View Article
Google Scholar

[170] View Article

[171] Google Scholar

[ref58] 58. McLachlan J. The effect of salinity on growth and chlorophyll content in representative classes of unicellular marine algae. Can J Microbiol. 1961;7:399–406.
View Article
Google Scholar

[173] View Article

[174] Google Scholar

[ref59] 59. Hughes DJ, Campbell DA, Doblin MA, Kromkamp JC, Lawrenz E, Moore CM, et al. Roadmaps and detours: Active chlorophyll- a assessments of primary productivity across marine and freshwater systems. Environ Sci Technol. 2018;52(21):12039–54. pmid:30247887
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref60] 60. Escribano-Gómez I, Pérez R, Arrizabalaga U, Liébana R, Vergara-Len M, López-Urrutia Á, et al. Synechococcus under stress: contrasting physiological and transcriptional responses to salinity and temperature in marine versus euryhaline strains. Environ Microbiol Rep. 2026;18(1):e70273. pmid:41587387
View Article
PubMed/NCBI
Google Scholar

[180] View Article

[181] PubMed/NCBI

[182] Google Scholar

[ref61] 61. Cabello-Yeves PJ, Callieri C, Picazo A, Schallenberg L, Huber P, Roda-Garcia JJ, et al. Elucidating the picocyanobacteria salinity divide through ecogenomics of new freshwater isolates. BMC Biol. 2022;20(1):175. pmid:35941649
View Article
PubMed/NCBI
Google Scholar

[184] View Article

[185] PubMed/NCBI

[186] Google Scholar

[ref62] 62. Finkel ZV, Beardall J, Flynn KJ, Quigg A, Rees TAV, Raven JA. Phytoplankton in a changing world: Cell size and elemental stoichiometry. J Plankton Res. 2009;32(1):119–37.
View Article
Google Scholar

[188] View Article

[189] Google Scholar

[ref63] 63. Liang Y, Zhang M, Wang M, Zhang W, Qiao C, Luo Q, et al. Freshwater cyanobacterium synechococcus elongatus PCC 7942 adapts to an environment with salt stress via ion-induced enzymatic balance of compatible solutes. Appl Environ Microbiol. 2020;86(7):e02904-19. pmid:31953341
View Article
PubMed/NCBI
Google Scholar

[191] View Article

[192] PubMed/NCBI

[193] Google Scholar

[ref64] 64. Xia X, Liao Y, Liu J, Leung SK, Lee PY, Zhang L, et al. Genomic and transcriptomic insights into salinity tolerance-based niche differentiation of synechococcus clades in estuarine and coastal waters. mSystems. 2023;8(1):e0110622. pmid:36622156
View Article
PubMed/NCBI
Google Scholar

[195] View Article

[196] PubMed/NCBI

[197] Google Scholar

[ref65] 65. Berthold M, Schumann R. Phosphorus dynamics in a eutrophic lagoon: uptake and utilization of nutrient pulses by phytoplankton. Front Mar Sci. 2020;7:281.
View Article
Google Scholar

[199] View Article

[200] Google Scholar

[ref66] 66. Schubert H, Fulda S, Hagemann M. Effects of adaptation to different salt concentrations on photosynthesis and pigmentation of the cyanobacterium synechocystis sp. PCC 6803. Journal of Plant Physiology. 1993;142(3):291–5.
View Article
Google Scholar

[202] View Article

[203] Google Scholar

[ref67] 67. Schmidt K, Birchill AJ, Atkinson A, Brewin RJW, Clark JR, Hickman AE, et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob Chang Biol. 2020;26(10):5574–87. pmid:32506810
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Strains, culturing, & media preparation

Experimental design; absorbance and fluorescence measurement regimes and data handling

Cell count & chlorophyll calibrations

Growth analyses

Photophysiology

Genomic analyses

Generalized additive modelling

Results

Salinity-light niches for chlorophyll- and biovolume-specific growth rates across strains

Discussion

Conclusions

Supporting information

S1 Table. Strain specific estimates to convert OD680-OD750 to μg chl mL-1 determined with a Turner fluorometer.

S2 Table. Strain-specific Biovolume estimates (μm3) from microscopy.

S3 Table. Gompertz Growth models were fit separately for each strain, salinity and plate replicate (n = 3) based on the pooled four (start of experiment) to two (end of experiment) technical replicate wells per plate for each salinity.

S4 Table. Genes probes used to search genomes.

S5 Table. Generalized additive models for chlorophyll specific growth of each strain, with salinity and light level as explanatory variables.

S6 Table. Generalized additive models for biovolume specific growth of each strain, with salinity and light level as explanatory variables.

S7 Table. Generalized additive models for photophysiological parameters FV/FM and metabolic capacity 1/tau (s-1) for each strain, with salinity and light level as explanatory variables.

S8 Table. Generalized additive models for photophysiological parameters FV/FM and metabolic capacity 1/tau (s-1) for each strain, with salinity and light level as explanatory variables.

S9 Table. Linear regression results for Biovolume-specific growth rates (d-1) with respect to FV/FM or 1/tau (s-1), with covariant growth media salinity (ppt), data pooled across strains and growth lights.

S10 Table. Summary of genes in studied picocyanobacteria, encoding proteins potentially involved in salt acclimation, detected using sequence probes derived from verified salt resistance proteins from different cyanobacteria and heterotrophic bacteria.

S11 Fig. Generalized additive models of CZS48M for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

S12 Fig. Generalized additive models of CZS25K for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

S13 Fig. Generalized additive models of CCMP1333 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

S14 Fig. Generalized additive models of NIES981 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

S15 Fig. Generalized additive models of CCMP836 for Chlorophyll, biovolume, cell number and integrated turbidity (400–700 nm) specific growth (A – D) across salinity and light levels.

References

S1 Table. Strain specific estimates to convert OD₆₈₀-OD₇₅₀ to μg chl mL^-1 determined with a Turner fluorometer.

S2 Table. Strain-specific Biovolume estimates (μm³) from microscopy.

S7 Table. Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) for each strain, with salinity and light level as explanatory variables.

S8 Table. Generalized additive models for photophysiological parameters F_V/F_M and metabolic capacity 1/tau (s^-1) for each strain, with salinity and light level as explanatory variables.

S9 Table. Linear regression results for Biovolume-specific growth rates (d^-1) with respect to F_V/F_M or 1/tau (s^-1), with covariant growth media salinity (ppt), data pooled across strains and growth lights.